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Database-Based Estimation of Liver Deformation under Pneumoperitoneum for Surgical Image-Guidance and Simulation

Part of the Lecture Notes in Computer Science book series (LNIP,volume 9350)


The insufflation of the abdomen in laparoscopic liver surgery leads to significant deformation of the liver. The estimation of the shape and position of the liver after insufflation has many important applications, such as providing surface-based registration algorithms used in image guidance with an initial guess and realistic patient-specific surgical simulation.

Our proposed algorithm computes a deformation estimate for a patient subject from a database of known insufflation deformations, as a weighted average. The database is built from pre-operative and intra-operative 3D image segmentations. The estimation pipeline also comprises a biomechanical simulation to incorporate patient-specific boundary conditions (BCs) and eliminate any non-physical deformation arising from the computation of the deformation as a weighted average.

We have evaluated the accuracy of our intra-subject registration, used for the computation of the displacements stored in the database, and our liver deformation predictions based on segmented, in-vivo porcine CT image data from 5 animals and manually selected vascular landmarks. We found root mean squared (RMS) target registration errors (TREs) of 2.96-11.31mm after intra-subject registration. For our estimated deformation, we found an RMS TRE of 5.82-11.47mm for four of the subjects, on one outlier subject the method failed.


  • Root Mean Square
  • Surgical Simulation
  • Target Registration Error
  • Laparoscopic Liver Surgery
  • Coherent Point Drift

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  1. Bano, J., et al.: Simulation of the abdominal wall and its arteries after pneumoperitoneum for guidance of port positioning in laparoscopic surgery. In: Bebis, G., et al. (eds.) ISVC 2012, Part I. LNCS, vol. 7431, pp. 1–11. Springer, Heidelberg (2012)

    CrossRef  Google Scholar 

  2. Bosman, J., Haouchine, N., Dequidt, J., Peterlik, I., Cotin, S., Duriez, C.: The role of ligaments: patient-specific or scenario-specific? In: Bello, F., Cotin, S. (eds.) ISBMS 2014. LNCS, vol. 8789, pp. 228–232. Springer, Heidelberg (2014)

    Google Scholar 

  3. Clements, L.W., Dumpuri, P., Chapman, W.C., Galloway, R.L., Miga, M.I.: Atlas-based method for model updating in image-guided liver surgery. In: Proceedings of SPIE, vol. 6509, pp. 650917–650917–12. SPIE (2007)

    Google Scholar 

  4. Jain, V., Zhang, H., Van Kaick, O.: Non-rigid spectral correspondence of triangle meshes. International Journal of Shape Modeling 13(01), 101–124 (2007)

    MathSciNet  CrossRef  MATH  Google Scholar 

  5. Kitasaka, T., Mori, K., Hayashi, Y., Suenaga, Y., Hashizume, M., Toriwaki, J.-I.: Virtual pneumoperitoneum for generating virtual laparoscopic views based on volumetric deformation. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3217, pp. 559–567. Springer, Heidelberg (2004)

    CrossRef  Google Scholar 

  6. Lombaert, H., Sporring, J., Siddiqi, K.: Diffeomorphic spectral matching of cortical surfaces. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds.) IPMI 2013. LNCS, vol. 7917, pp. 376–389. Springer, Heidelberg (2013)

    CrossRef  Google Scholar 

  7. Modat, M., Daga, P., Cardoso, M.J., Ourselin, S., Ridgway, G.R., Ashburner, J.: Parametric non-rigid registration using a stationary velocity field. In: Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis, pp. 145–150 (2012)

    Google Scholar 

  8. Myronenko, A., Song, X.: Point set registration: coherent point drift. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(12), 2262–2275 (2010)

    CrossRef  Google Scholar 

  9. Plantefève, R., Peterlik, I., Courtecuisse, H., Trivisonne, R., Radoux, J.-P., Cotin, S.: Atlas-based transfer of boundary conditions for biomechanical simulation. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014, Part II. LNCS, vol. 8674, pp. 33–40. Springer, Heidelberg (2014)

    Google Scholar 

  10. Totz, J., Thompson, S., Stoyanov, D., Gurusamy, K., Davidson, B.R., Hawkes, D.J., Clarkson, M.J.: Fast semi-dense surface reconstruction from stereoscopic video in laparoscopic surgery. In: Stoyanov, D., Collins, D.L., Sakuma, I., Abolmaesumi, P., Jannin, P. (eds.) IPCAI 2014. LNCS, vol. 8498, pp. 206–215. Springer, Heidelberg (2014)

    CrossRef  Google Scholar 

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Johnsen, S.F. et al. (2015). Database-Based Estimation of Liver Deformation under Pneumoperitoneum for Surgical Image-Guidance and Simulation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science(), vol 9350. Springer, Cham.

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